构建人机协同需求工程框架，提升需求获取效率与透明度
Towards Human-AI Synergy in Requirements Engineering: A Framework and Preliminary Study
- 提出人机协同需求工程模型（HARE-SM），融合AI分析与人工监督
- 强调透明性与可解释性，缓解算法偏见与自动化伦理风险
- 适合关注AI辅助需求工程的开发团队与研究者
未来的需求工程（RE）正日益由人工智能驱动，重塑需求获取、分析与验证方式。传统方法依赖人力，易出错且复杂。大语言模型（LLMs）、自然语言处理（NLP）和生成式AI等技术提供了高效解决方案。然而，其应用也带来算法偏见、缺乏可解释性及自动化伦理问题。为此，本研究提出人机协同需求工程模型（HARE-SM），通过整合AI分析与人工监督，提升需求获取、分析与验证质量。该模型强调透明、可解释与偏见缓解，推动伦理化使用AI。研究设计多阶段方法：准备RE数据集、微调AI模型、构建协作型人机工作流。本初步研究呈现概念框架与早期原型实现，为智能数据分析技术在半结构化与非结构化需求数据中的协同应用奠定研究基础与实践方向。
原文摘要 · Abstract (English)
The future of Requirements Engineering (RE) is increasingly driven by artificial intelligence (AI), reshaping how we elicit, analyze, and validate requirements. Traditional RE is based on labor-intensive manual processes prone to errors and complexity. AI-powered approaches, specifically large language models (LLMs), natural language processing (NLP), and generative AI, offer transformative solutions and reduce inefficiencies. However, the use of AI in RE also brings challenges like algorithmic bias, lack of explainability, and ethical concerns related to automation. To address these issues, this study introduces the Human-AI RE Synergy Model (HARE-SM), a conceptual framework that integrates AI-driven analysis with human oversight to improve requirements elicitation, analysis, and validation. The model emphasizes ethical AI use through transparency, explainability, and bias mitigation. We outline a multi-phase research methodology focused on preparing RE datasets, fine-tuning AI models, and designing collaborative human-AI workflows. This preliminary study presents the conceptual framework and early-stage prototype implementation, establishing a research agenda and practical design direction for applying intelligent data science techniques to semi-structured and unstructured RE data in collaborative environments.
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